We follow the approach of [1]} to compute CAMs of training images as initial segmentation labels. The architecture is a typical classification network with global average pooling (GAP) followed by a fully connected layer, which is trained with image-level labels. Given an image, the CAM of a ground truth class \(c\)  is computed by:
\({{M}_{c}}(x,y)=\sum \limits _{k}{\omega _{k}^{c}{{f}_{k}}(x,y)}\) 
